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Auto-generate distro yamls + docs (#468)

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# What does this PR do?

Automatically generates
- build.yaml
- run.yaml
- run-with-safety.yaml
- parts of markdown docs

for the distributions.

## Test Plan

At this point, this only updates the YAMLs and the docs. Some testing
(especially with ollama and vllm) has been performed but needs to be
much more tested.
This commit is contained in:
Ashwin Bharambe 2024-11-18 14:57:06 -08:00 • committed by GitHub
parent 0784284ab5
commit 2a31163178
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
88 changed files with 3008 additions and 852 deletions
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21
llama_stack/cli/stack/build.py
View file

@ -193,7 +193,6 @@ class StackBuild(Subcommand):
apis = list(build_config.distribution_spec.providers.keys())
run_config = StackRunConfig(
built_at=datetime.now(),
docker_image=(
build_config.name
if build_config.image_type == ImageType.docker.value
@ -217,15 +216,23 @@ class StackBuild(Subcommand):
provider_types = [provider_types]
for i, provider_type in enumerate(provider_types):
p_spec = Provider(
provider_id=f"{provider_type}-{i}",
provider_type=provider_type,
config={},
)
pid = provider_type.split("::")[-1]
config_type = instantiate_class_type(
provider_registry[Api(api)][provider_type].config_class
)
p_spec.config = config_type()
if hasattr(config_type, "sample_run_config"):
config = config_type.sample_run_config(
__distro_dir__=f"distributions/{build_config.name}"
)
else:
config = {}
p_spec = Provider(
provider_id=f"{pid}-{i}" if len(provider_types) > 1 else pid,
provider_type=provider_type,
config=config,
)
run_config.providers[api].append(p_spec)
os.makedirs(build_dir, exist_ok=True)

19
llama_stack/cli/stack/run.py
View file

@ -39,6 +39,13 @@ class StackRun(Subcommand):
help="Disable IPv6 support",
default=False,
)
self.parser.add_argument(
"--env",
action="append",
help="Environment variables to pass to the server in KEY=VALUE format. Can be specified multiple times.",
default=[],
metavar="KEY=VALUE",
)
def _run_stack_run_cmd(self, args: argparse.Namespace) -> None:
from pathlib import Path
@ -108,4 +115,16 @@ class StackRun(Subcommand):
if args.disable_ipv6:
run_args.append("--disable-ipv6")
for env_var in args.env:
if "=" not in env_var:
self.parser.error(
f"Environment variable '{env_var}' must be in KEY=VALUE format"
)
return
key, value = env_var.split("=", 1) # split on first = only
if not key:
self.parser.error(f"Environment variable '{env_var}' has empty key")
return
run_args.extend(["--env", f"{key}={value}"])
run_with_pty(run_args)

2
llama_stack/distribution/build_container.sh
View file

@ -146,6 +146,8 @@ fi
# Set version tag based on PyPI version
if [ -n "$TEST_PYPI_VERSION" ]; then
version_tag="test-$TEST_PYPI_VERSION"
elif [[ -n "$LLAMA_STACK_DIR" || -n "$LLAMA_MODELS_DIR" ]]; then
version_tag="dev"
else
URL="https://pypi.org/pypi/llama-stack/json"
version_tag=$(curl -s $URL | jq -r '.info.version')

3
llama_stack/distribution/datatypes.py
View file

@ -4,8 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from datetime import datetime
from typing import Dict, List, Optional, Union
from pydantic import BaseModel, Field
@ -115,7 +113,6 @@ class Provider(BaseModel):
class StackRunConfig(BaseModel):
version: str = LLAMA_STACK_RUN_CONFIG_VERSION
built_at: datetime
image_name: str = Field(
...,

3
llama_stack/distribution/server/server.py
View file

@ -313,7 +313,8 @@ def replace_env_vars(config: Any, path: str = "") -> Any:
else:
value = default_val
return value
# expand "~" from the values
return os.path.expanduser(value)
try:
return re.sub(pattern, get_env_var, config)

25
llama_stack/distribution/start_conda_env.sh
View file

@ -33,10 +33,33 @@ shift
port="$1"
shift
# Process environment variables from --env arguments
env_vars=""
while [[ $# -gt 0 ]]; do
case "$1" in
--env)
if [[ -n "$2" ]]; then
# collect environment variables so we can set them after activating the conda env
env_vars="$env_vars $2"
shift 2
else
echo -e "${RED}Error: --env requires a KEY=VALUE argument${NC}" >&2
exit 1
fi
;;
*)
shift
;;
esac
done
eval "$(conda shell.bash hook)"
conda deactivate && conda activate "$env_name"
$CONDA_PREFIX/bin/python \
set -x
$env_vars \
$CONDA_PREFIX/bin/python \
-m llama_stack.distribution.server.server \
--yaml_config "$yaml_config" \
--port "$port" "$@"

27
llama_stack/distribution/start_container.sh
View file

@ -31,7 +31,7 @@ if [ $# -lt 3 ]; then
fi
build_name="$1"
docker_image="distribution-$build_name"
docker_image="localhost/distribution-$build_name"
shift
yaml_config="$1"
@ -40,6 +40,26 @@ shift
port="$1"
shift
# Process environment variables from --env arguments
env_vars=""
while [[ $# -gt 0 ]]; do
case "$1" in
--env)
echo "env = $2"
if [[ -n "$2" ]]; then
env_vars="$env_vars -e $2"
shift 2
else
echo -e "${RED}Error: --env requires a KEY=VALUE argument${NC}" >&2
exit 1
fi
;;
*)
shift
;;
esac
done
set -x
if command -v selinuxenabled &> /dev/null && selinuxenabled; then
@ -59,15 +79,18 @@ fi
version_tag="latest"
if [ -n "$PYPI_VERSION" ]; then
version_tag="$PYPI_VERSION"
elif [ -n "$LLAMA_STACK_DIR" ]; then
version_tag="dev"
elif [ -n "$TEST_PYPI_VERSION" ]; then
version_tag="test-$TEST_PYPI_VERSION"
fi
$DOCKER_BINARY run $DOCKER_OPTS -it \
-p $port:$port \
$env_vars \
-v "$yaml_config:/app/config.yaml" \
$mounts \
$docker_image:$version_tag \
python -m llama_stack.distribution.server.server \
--yaml_config /app/config.yaml \
--port $port "$@"
--port "$port"

15
llama_stack/providers/inline/agents/meta_reference/config.py
View file

@ -4,11 +4,22 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pydantic import BaseModel, Field
from typing import Any, Dict
from pydantic import BaseModel
from llama_stack.providers.utils.kvstore import KVStoreConfig
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
class MetaReferenceAgentsImplConfig(BaseModel):
persistence_store: KVStoreConfig = Field(default=SqliteKVStoreConfig())
persistence_store: KVStoreConfig
@classmethod
def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
return {
"persistence_store": SqliteKVStoreConfig.sample_run_config(
__distro_dir__=__distro_dir__,
db_name="agents_store.db",
)
}

12
llama_stack/providers/inline/inference/meta_reference/config.py
View file

@ -49,6 +49,18 @@ class MetaReferenceInferenceConfig(BaseModel):
resolved = resolve_model(self.model)
return resolved.pth_file_count
@classmethod
def sample_run_config(
cls,
model: str = "Llama3.2-3B-Instruct",
checkpoint_dir: str = "${env.CHECKPOINT_DIR:null}",
) -> Dict[str, Any]:
return {
"model": model,
"max_seq_len": 4096,
"checkpoint_dir": checkpoint_dir,
}
class MetaReferenceQuantizedInferenceConfig(MetaReferenceInferenceConfig):
quantization: QuantizationConfig

3
llama_stack/providers/inline/inference/meta_reference/generation.py
View file

@ -107,7 +107,7 @@ class Llama:
sys.stdout = open(os.devnull, "w")
start_time = time.time()
if config.checkpoint_dir:
if config.checkpoint_dir and config.checkpoint_dir != "null":
ckpt_dir = config.checkpoint_dir
else:
ckpt_dir = model_checkpoint_dir(model)
@ -137,7 +137,6 @@ class Llama:
), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
if isinstance(config, MetaReferenceQuantizedInferenceConfig):
if isinstance(config.quantization, Fp8QuantizationConfig):
from .quantization.loader import convert_to_fp8_quantized_model

10
llama_stack/providers/inline/inference/vllm/config.py
View file

@ -34,6 +34,16 @@ class VLLMConfig(BaseModel):
default=0.3,
)
@classmethod
def sample_run_config(cls):
return {
"model": "${env.VLLM_INFERENCE_MODEL:Llama3.2-3B-Instruct}",
"tensor_parallel_size": "${env.VLLM_TENSOR_PARALLEL_SIZE:1}",
"max_tokens": "${env.VLLM_MAX_TOKENS:4096}",
"enforce_eager": "${env.VLLM_ENFORCE_EAGER:False}",
"gpu_memory_utilization": "${env.VLLM_GPU_MEMORY_UTILIZATION:0.3}",
}
@field_validator("model")
@classmethod
def validate_model(cls, model: str) -> str:

16
llama_stack/providers/inline/memory/faiss/config.py
View file

@ -4,10 +4,11 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel
from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
from llama_stack.providers.utils.kvstore.config import (
KVStoreConfig,
SqliteKVStoreConfig,
@ -16,6 +17,13 @@ from llama_stack.providers.utils.kvstore.config import (
@json_schema_type
class FaissImplConfig(BaseModel):
kvstore: KVStoreConfig = SqliteKVStoreConfig(
db_path=(RUNTIME_BASE_DIR / "faiss_store.db").as_posix()
) # Uses SQLite config specific to FAISS storage
kvstore: KVStoreConfig
@classmethod
def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
return {
"kvstore": SqliteKVStoreConfig.sample_run_config(
__distro_dir__=__distro_dir__,
db_name="faiss_store.db",
)
}

26
llama_stack/providers/inline/safety/llama_guard/llama_guard.py
View file

@ -73,18 +73,21 @@ DEFAULT_LG_V3_SAFETY_CATEGORIES = [
CAT_ELECTIONS,
]
LLAMA_GUARD_MODEL_IDS = [
CoreModelId.llama_guard_3_8b.value,
CoreModelId.llama_guard_3_1b.value,
CoreModelId.llama_guard_3_11b_vision.value,
]
# accept both CoreModelId and huggingface repo id
LLAMA_GUARD_MODEL_IDS = {
CoreModelId.llama_guard_3_8b.value: "meta-llama/Llama-Guard-3-8B",
"meta-llama/Llama-Guard-3-8B": "meta-llama/Llama-Guard-3-8B",
CoreModelId.llama_guard_3_1b.value: "meta-llama/Llama-Guard-3-1B",
"meta-llama/Llama-Guard-3-1B": "meta-llama/Llama-Guard-3-1B",
CoreModelId.llama_guard_3_11b_vision.value: "meta-llama/Llama-Guard-3-11B-Vision",
"meta-llama/Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision",
}
MODEL_TO_SAFETY_CATEGORIES_MAP = {
CoreModelId.llama_guard_3_8b.value: (
DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
),
CoreModelId.llama_guard_3_1b.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
CoreModelId.llama_guard_3_11b_vision.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
"meta-llama/Llama-Guard-3-8B": DEFAULT_LG_V3_SAFETY_CATEGORIES
+ [CAT_CODE_INTERPRETER_ABUSE],
"meta-llama/Llama-Guard-3-1B": DEFAULT_LG_V3_SAFETY_CATEGORIES,
"meta-llama/Llama-Guard-3-11B-Vision": DEFAULT_LG_V3_SAFETY_CATEGORIES,
}
@ -150,8 +153,9 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
if len(messages) > 0 and messages[0].role != Role.user.value:
messages[0] = UserMessage(content=messages[0].content)
model = LLAMA_GUARD_MODEL_IDS[shield.provider_resource_id]
impl = LlamaGuardShield(
model=shield.provider_resource_id,
model=model,
inference_api=self.inference_api,
excluded_categories=self.config.excluded_categories,
)

9
llama_stack/providers/remote/inference/fireworks/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@ -20,3 +20,10 @@ class FireworksImplConfig(BaseModel):
default=None,
description="The Fireworks.ai API Key",
)
@classmethod
def sample_run_config(cls) -> Dict[str, Any]:
return {
"url": "https://api.fireworks.ai/inference",
"api_key": "${env.FIREWORKS_API_KEY}",
}

4
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -35,7 +35,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import FireworksImplConfig
model_aliases = [
MODEL_ALIASES = [
build_model_alias(
"fireworks/llama-v3p1-8b-instruct",
CoreModelId.llama3_1_8b_instruct.value,
@ -79,7 +79,7 @@ class FireworksInferenceAdapter(
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: FireworksImplConfig) -> None:
ModelRegistryHelper.__init__(self, model_aliases)
ModelRegistryHelper.__init__(self, MODEL_ALIASES)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())

8
llama_stack/providers/remote/inference/ollama/__init__.py
View file

@ -4,14 +4,10 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.distribution.datatypes import RemoteProviderConfig
from .config import OllamaImplConfig
class OllamaImplConfig(RemoteProviderConfig):
port: int = 11434
async def get_adapter_impl(config: RemoteProviderConfig, _deps):
async def get_adapter_impl(config: OllamaImplConfig, _deps):
from .ollama import OllamaInferenceAdapter
impl = OllamaInferenceAdapter(config.url)

22
llama_stack/providers/remote/inference/ollama/config.py Normal file
View file

@ -0,0 +1,22 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict
from pydantic import BaseModel
DEFAULT_OLLAMA_URL = "http://localhost:11434"
class OllamaImplConfig(BaseModel):
url: str = DEFAULT_OLLAMA_URL
@classmethod
def sample_run_config(
cls, url: str = "${env.OLLAMA_URL:http://localhost:11434}", **kwargs
) -> Dict[str, Any]:
return {"url": url}

2
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -82,7 +82,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
return AsyncClient(host=self.url)
async def initialize(self) -> None:
print("Initializing Ollama, checking connectivity to server...")
print(f"checking connectivity to Ollama at `{self.url}`...")
try:
await self.client.ps()
except httpx.ConnectError as e:

17
llama_stack/providers/remote/inference/tgi/config.py
View file

@ -12,19 +12,20 @@ from pydantic import BaseModel, Field
@json_schema_type
class TGIImplConfig(BaseModel):
host: str = "localhost"
port: int = 8080
protocol: str = "http"
@property
def url(self) -> str:
return f"{self.protocol}://{self.host}:{self.port}"
url: str = Field(
description="The URL for the TGI serving endpoint",
)
api_token: Optional[str] = Field(
default=None,
description="A bearer token if your TGI endpoint is protected.",
)
@classmethod
def sample_run_config(cls, url: str = "${env.TGI_URL}", **kwargs):
return {
"url": url,
}
@json_schema_type
class InferenceEndpointImplConfig(BaseModel):

9
llama_stack/providers/remote/inference/together/config.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from typing import Any, Dict, Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@ -20,3 +20,10 @@ class TogetherImplConfig(BaseModel):
default=None,
description="The Together AI API Key",
)
@classmethod
def sample_run_config(cls) -> Dict[str, Any]:
return {
"url": "https://api.together.xyz/v1",
"api_key": "${env.TOGETHER_API_KEY}",
}

4
llama_stack/providers/remote/inference/together/together.py
View file

@ -38,7 +38,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import TogetherImplConfig
model_aliases = [
MODEL_ALIASES = [
build_model_alias(
"meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
CoreModelId.llama3_1_8b_instruct.value,
@ -78,7 +78,7 @@ class TogetherInferenceAdapter(
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: TogetherImplConfig) -> None:
ModelRegistryHelper.__init__(self, model_aliases)
ModelRegistryHelper.__init__(self, MODEL_ALIASES)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())

12
llama_stack/providers/remote/inference/vllm/config.py
View file

@ -24,3 +24,15 @@ class VLLMInferenceAdapterConfig(BaseModel):
default="fake",
description="The API token",
)
@classmethod
def sample_run_config(
cls,
url: str = "${env.VLLM_URL}",
**kwargs,
):
return {
"url": url,
"max_tokens": "${env.VLLM_MAX_TOKENS:4096}",
"api_token": "${env.VLLM_API_TOKEN:fake}",
}

2
llama_stack/providers/tests/resolver.py
View file

@ -6,7 +6,6 @@
import json
import tempfile
from datetime import datetime
from typing import Any, Dict, List, Optional
from llama_stack.distribution.datatypes import * # noqa: F403
@ -37,7 +36,6 @@ async def construct_stack_for_test(
) -> TestStack:
sqlite_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db")
run_config = dict(
built_at=datetime.now(),
image_name="test-fixture",
apis=apis,
providers=providers,

35
llama_stack/providers/utils/kvstore/config.py
View file

@ -36,6 +36,15 @@ class RedisKVStoreConfig(CommonConfig):
def url(self) -> str:
return f"redis://{self.host}:{self.port}"
@classmethod
def sample_run_config(cls):
return {
"type": "redis",
"namespace": None,
"host": "${env.REDIS_HOST:localhost}",
"port": "${env.REDIS_PORT:6379}",
}
class SqliteKVStoreConfig(CommonConfig):
type: Literal[KVStoreType.sqlite.value] = KVStoreType.sqlite.value
@ -44,6 +53,19 @@ class SqliteKVStoreConfig(CommonConfig):
description="File path for the sqlite database",
)
@classmethod
def sample_run_config(
cls, __distro_dir__: str = "runtime", db_name: str = "kvstore.db"
):
return {
"type": "sqlite",
"namespace": None,
"db_path": "${env.SQLITE_STORE_DIR:~/.llama/"
+ __distro_dir__
+ "}/"
+ db_name,
}
class PostgresKVStoreConfig(CommonConfig):
type: Literal[KVStoreType.postgres.value] = KVStoreType.postgres.value
@ -54,6 +76,19 @@ class PostgresKVStoreConfig(CommonConfig):
password: Optional[str] = None
table_name: str = "llamastack_kvstore"
@classmethod
def sample_run_config(cls, table_name: str = "llamastack_kvstore"):
return {
"type": "postgres",
"namespace": None,
"host": "${env.POSTGRES_HOST:localhost}",
"port": "${env.POSTGRES_PORT:5432}",
"db": "${env.POSTGRES_DB}",
"user": "${env.POSTGRES_USER}",
"password": "${env.POSTGRES_PASSWORD}",
"table_name": "${env.POSTGRES_TABLE_NAME:" + table_name + "}",
}
@classmethod
@field_validator("table_name")
def validate_table_name(cls, v: str) -> str:

81
llama_stack/scripts/distro_codegen.py Normal file
View file

@ -0,0 +1,81 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import concurrent.futures
import importlib
from functools import partial
from pathlib import Path
from typing import Iterator
from rich.progress import Progress, SpinnerColumn, TextColumn
REPO_ROOT = Path(__file__).parent.parent.parent
def find_template_dirs(templates_dir: Path) -> Iterator[Path]:
"""Find immediate subdirectories in the templates folder."""
if not templates_dir.exists():
raise FileNotFoundError(f"Templates directory not found: {templates_dir}")
return (
d for d in templates_dir.iterdir() if d.is_dir() and d.name != "__pycache__"
)
def process_template(template_dir: Path, progress) -> None:
"""Process a single template directory."""
progress.print(f"Processing {template_dir.name}")
try:
# Import the module directly
module_name = f"llama_stack.templates.{template_dir.name}"
module = importlib.import_module(module_name)
# Get and save the distribution template
if template_func := getattr(module, "get_distribution_template", None):
template = template_func()
template.save_distribution(
yaml_output_dir=REPO_ROOT / "llama_stack" / "templates" / template.name,
doc_output_dir=REPO_ROOT
/ "docs/source/getting_started/distributions"
/ f"{template.distro_type}_distro",
)
else:
progress.print(
f"[yellow]Warning: {template_dir.name} has no get_distribution_template function"
)
except Exception as e:
progress.print(f"[red]Error processing {template_dir.name}: {str(e)}")
raise e
def main():
templates_dir = REPO_ROOT / "llama_stack" / "templates"
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
) as progress:
template_dirs = list(find_template_dirs(templates_dir))
task = progress.add_task(
"Processing distribution templates...", total=len(template_dirs)
)
# Create a partial function with the progress bar
process_func = partial(process_template, progress=progress)
# Process templates in parallel
with concurrent.futures.ThreadPoolExecutor() as executor:
# Submit all tasks and wait for completion
list(executor.map(process_func, template_dirs))
progress.update(task, advance=len(template_dirs))
if __name__ == "__main__":
main()

5
llama_stack/templates/__init__.py Normal file
View file

@ -0,0 +1,5 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

7
llama_stack/templates/fireworks/__init__.py Normal file
View file

@ -0,0 +1,7 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .fireworks import get_distribution_template # noqa: F401

20
llama_stack/templates/fireworks/build.yaml
View file

@ -1,11 +1,19 @@
version: '2'
name: fireworks
distribution_spec:
description: Use Fireworks.ai for running LLM inference
description: Use Fireworks.AI for running LLM inference
docker_image: null
providers:
inference: remote::fireworks
inference:
- remote::fireworks
memory:
- inline::faiss
- remote::weaviate
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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# Fireworks Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }}`
{% endfor %}
{% endif %}
### Prerequisite: API Keys
Make sure you have access to a Fireworks API Key. You can get one by visiting [fireworks.ai](https://fireworks.ai/).
## Running Llama Stack with Fireworks
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```
### Via Conda
```bash
llama stack build --template fireworks --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env FIREWORKS_API_KEY=$FIREWORKS_API_KEY
```

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.fireworks import MODEL_ALIASES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::fireworks"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="fireworks",
provider_type="remote::fireworks",
config=FireworksImplConfig.sample_run_config(),
)
default_models = [ModelInput(model_id=m.provider_model_id) for m in MODEL_ALIASES]
return DistributionTemplate(
name="fireworks",
distro_type="self_hosted",
description="Use Fireworks.AI for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=default_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=default_models,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (
"",
"Fireworks.AI API Key",
),
},
)

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version: '2'
image_name: fireworks
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: fireworks
provider_type: remote::fireworks
config:
url: https://api.fireworks.ai/inference
api_key: ${env.FIREWORKS_API_KEY}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/registry.db
models:
- metadata: {}
model_id: fireworks/llama-v3p1-8b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p1-70b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p1-405b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-1b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-3b-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-11b-vision-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-v3p2-90b-vision-instruct
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-guard-3-8b
provider_id: null
provider_model_id: null
- metadata: {}
model_id: fireworks/llama-guard-3-11b-vision
provider_id: null
provider_model_id: null
shields:
- params: null
shield_id: meta-llama/Llama-Guard-3-8B
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .meta_reference import get_distribution_template # noqa: F401

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version: '2'
name: meta-reference-gpu
distribution_spec:
docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
description: Use code from `llama_stack` itself to serve all llama stack APIs
description: Use Meta Reference for running LLM inference
docker_image: null
providers:
inference: meta-reference
inference:
- inline::meta-reference
memory:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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# Meta Reference Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations:
{{ providers_table }}
Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
```
## Running the Distribution
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template meta-reference-gpu --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.inline.inference.meta_reference import (
MetaReferenceInferenceConfig,
)
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["inline::meta-reference"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="meta-reference-inference",
provider_type="inline::meta-reference",
config=MetaReferenceInferenceConfig.sample_run_config(
model="${env.INFERENCE_MODEL}",
checkpoint_dir="${env.INFERENCE_CHECKPOINT_DIR:null}",
),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="meta-reference-inference",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="meta-reference-safety",
)
return DistributionTemplate(
name="meta-reference-gpu",
distro_type="self_hosted",
description="Use Meta Reference for running LLM inference",
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=[inference_model, safety_model],
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=[inference_model],
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
Provider(
provider_id="meta-reference-safety",
provider_type="inline::meta-reference",
config=MetaReferenceInferenceConfig.sample_run_config(
model="${env.SAFETY_MODEL}",
checkpoint_dir="${env.SAFETY_CHECKPOINT_DIR:null}",
),
),
],
},
default_models=[
inference_model,
safety_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
),
},
docker_compose_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the Meta Reference server",
),
"INFERENCE_CHECKPOINT_DIR": (
"null",
"Directory containing the Meta Reference model checkpoint",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Name of the safety (Llama-Guard) model to use",
),
"SAFETY_CHECKPOINT_DIR": (
"null",
"Directory containing the Llama-Guard model checkpoint",
),
},
)

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version: '2'
image_name: meta-reference-gpu
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: meta-reference-inference
provider_type: inline::meta-reference
config:
model: ${env.INFERENCE_MODEL}
max_seq_len: 4096
checkpoint_dir: ${env.INFERENCE_CHECKPOINT_DIR:null}
- provider_id: meta-reference-safety
provider_type: inline::meta-reference
config:
model: ${env.SAFETY_MODEL}
max_seq_len: 4096
checkpoint_dir: ${env.SAFETY_CHECKPOINT_DIR:null}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: meta-reference-inference
provider_model_id: null
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: meta-reference-safety
provider_model_id: null
shields:
- params: null
shield_id: ${env.SAFETY_MODEL}
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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version: '2'
image_name: meta-reference-gpu
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: meta-reference-inference
provider_type: inline::meta-reference
config:
model: ${env.INFERENCE_MODEL}
max_seq_len: 4096
checkpoint_dir: ${env.INFERENCE_CHECKPOINT_DIR:null}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: meta-reference-inference
provider_model_id: null
shields: []
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .ollama import get_distribution_template # noqa: F401

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version: '2'
name: ollama
distribution_spec:
description: Use ollama for running LLM inference
description: Use (an external) Ollama server for running LLM inference
docker_image: null
providers:
inference: remote::ollama
inference:
- remote::ollama
memory:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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# Ollama Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.
{%- if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Setting up Ollama server
Please check the [Ollama Documentation](https://github.com/ollama/ollama) on how to install and run Ollama. After installing Ollama, you need to run `ollama serve` to start the server.
In order to load models, you can run:
```bash
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
# ollama names this model differently, and we must use the ollama name when loading the model
export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
```
If you are using Llama Stack Safety / Shield APIs, you will also need to pull and run the safety model.
```bash
export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
# ollama names this model differently, and we must use the ollama name when loading the model
export OLLAMA_SAFETY_MODEL="llama-guard3:1b"
ollama run $OLLAMA_SAFETY_MODEL --keepalive 60m
```
## Running Llama Stack
Now you are ready to run Llama Stack with Ollama as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./run.yaml:/root/my-run.yaml \
--gpus=all \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-v ./run-with-safety.yaml:/root/my-run.yaml \
--gpus=all \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template ollama --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://127.0.0.1:11434
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=http://127.0.0.1:11434
```
### (Optional) Update Model Serving Configuration
> [!NOTE]
> Please check the [OLLAMA_SUPPORTED_MODELS](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers.remote/inference/ollama/ollama.py) for the supported Ollama models.
To serve a new model with `ollama`
```bash
ollama run <model_name>
```
To make sure that the model is being served correctly, run `ollama ps` to get a list of models being served by ollama.
```
$ ollama ps
NAME ID SIZE PROCESSOR UNTIL
llama3.1:8b-instruct-fp16 4aacac419454 17 GB 100% GPU 4 minutes from now
```
To verify that the model served by ollama is correctly connected to Llama Stack server
```bash
$ llama-stack-client models list
+----------------------+----------------------+---------------+-----------------------------------------------+
| identifier | llama_model | provider_id | metadata |
+======================+======================+===============+===============================================+
| Llama3.1-8B-Instruct | Llama3.1-8B-Instruct | ollama0 | {'ollama_model': 'llama3.1:8b-instruct-fp16'} |
+----------------------+----------------------+---------------+-----------------------------------------------+
```

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.remote.inference.ollama import OllamaImplConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::ollama"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="ollama",
provider_type="remote::ollama",
config=OllamaImplConfig.sample_run_config(),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="ollama",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="ollama",
)
return DistributionTemplate(
name="ollama",
distro_type="self_hosted",
description="Use (an external) Ollama server for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=[inference_model, safety_model],
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=[inference_model],
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
]
},
default_models=[
inference_model,
safety_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
),
},
docker_compose_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"OLLAMA_URL": (
"http://127.0.0.1:11434",
"URL of the Ollama server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the Ollama server",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Safety model loaded into the Ollama server",
),
},
)

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version: '2'
image_name: ollama
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: ollama
provider_type: remote::ollama
config:
url: ${env.OLLAMA_URL:http://localhost:11434}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: ollama
provider_model_id: null
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: ollama
provider_model_id: null
shields:
- params: null
shield_id: ${env.SAFETY_MODEL}
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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version: '2'
image_name: ollama
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: ollama
provider_type: remote::ollama
config:
url: ${env.OLLAMA_URL:http://localhost:11434}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: ollama
provider_model_id: null
shields: []
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .vllm import get_distribution_template # noqa: F401

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version: '2'
name: remote-vllm
distribution_spec:
description: Use (an external) vLLM server for running LLM inference
docker_image: null
providers:
inference: remote::vllm
inference:
- remote::vllm
memory:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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# Remote vLLM Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations:
{{ providers_table }}
You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Setting up vLLM server
Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) to get a vLLM endpoint. Here is a sample script to start a vLLM server locally via Docker:
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--model $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run \
--runtime nvidia \
--gpus $CUDA_VISIBLE_DEVICES \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p $SAFETY_PORT:$SAFETY_PORT \
--ipc=host \
vllm/vllm-openai:latest \
--model $SAFETY_MODEL \
--port $SAFETY_PORT
```
## Running Llama Stack
Now you are ready to run Llama Stack with vLLM as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT \
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env VLLM_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template remote-vllm --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env VLLM_URL=http://127.0.0.1:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env VLLM_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

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version: '2'
image_name: remote-vllm
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: vllm-inference
provider_type: remote::vllm
config:
url: ${env.VLLM_URL}
max_tokens: ${env.VLLM_MAX_TOKENS:4096}
api_token: ${env.VLLM_API_TOKEN:fake}
- provider_id: vllm-safety
provider_type: remote::vllm
config:
url: ${env.SAFETY_VLLM_URL}
max_tokens: ${env.VLLM_MAX_TOKENS:4096}
api_token: ${env.VLLM_API_TOKEN:fake}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-inference
provider_model_id: null
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: vllm-safety
provider_model_id: null
shields:
- params: null
shield_id: ${env.SAFETY_MODEL}
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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version: '2'
image_name: remote-vllm
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: vllm-inference
provider_type: remote::vllm
config:
url: ${env.VLLM_URL}
max_tokens: ${env.VLLM_MAX_TOKENS:4096}
api_token: ${env.VLLM_API_TOKEN:fake}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-inference
provider_model_id: null
shields: []
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::vllm"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="vllm-inference",
provider_type="remote::vllm",
config=VLLMInferenceAdapterConfig.sample_run_config(
url="${env.VLLM_URL}",
),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="vllm-inference",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="vllm-safety",
)
return DistributionTemplate(
name="remote-vllm",
distro_type="self_hosted",
description="Use (an external) vLLM server for running LLM inference",
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=[inference_model, safety_model],
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=[inference_model],
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
Provider(
provider_id="vllm-safety",
provider_type="remote::vllm",
config=VLLMInferenceAdapterConfig.sample_run_config(
url="${env.SAFETY_VLLM_URL}",
),
),
],
},
default_models=[
inference_model,
safety_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
),
},
docker_compose_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the vLLM server",
),
"VLLM_URL": (
"http://host.docker.internal:5100}/v1",
"URL of the vLLM server with the main inference model",
),
"MAX_TOKENS": (
"4096",
"Maximum number of tokens for generation",
),
"SAFETY_VLLM_URL": (
"http://host.docker.internal:5101/v1",
"URL of the vLLM server with the safety model",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Name of the safety (Llama-Guard) model to use",
),
},
)

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from typing import Dict, List, Literal, Optional, Tuple
import jinja2
import yaml
from pydantic import BaseModel, Field
from llama_stack.distribution.datatypes import (
Api,
BuildConfig,
DistributionSpec,
ModelInput,
Provider,
ShieldInput,
StackRunConfig,
)
from llama_stack.distribution.distribution import get_provider_registry
from llama_stack.distribution.utils.dynamic import instantiate_class_type
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
class RunConfigSettings(BaseModel):
provider_overrides: Dict[str, List[Provider]] = Field(default_factory=dict)
default_models: List[ModelInput]
default_shields: Optional[List[ShieldInput]] = None
def run_config(
self,
name: str,
providers: Dict[str, List[str]],
docker_image: Optional[str] = None,
) -> StackRunConfig:
provider_registry = get_provider_registry()
provider_configs = {}
for api_str, provider_types in providers.items():
if api_providers := self.provider_overrides.get(api_str):
provider_configs[api_str] = api_providers
continue
provider_type = provider_types[0]
provider_id = provider_type.split("::")[-1]
api = Api(api_str)
if provider_type not in provider_registry[api]:
raise ValueError(
f"Unknown provider type: {provider_type} for API: {api_str}"
)
config_class = provider_registry[api][provider_type].config_class
assert (
config_class is not None
), f"No config class for provider type: {provider_type} for API: {api_str}"
config_class = instantiate_class_type(config_class)
if hasattr(config_class, "sample_run_config"):
config = config_class.sample_run_config(
__distro_dir__=f"distributions/{name}"
)
else:
config = {}
provider_configs[api_str] = [
Provider(
provider_id=provider_id,
provider_type=provider_type,
config=config,
)
]
# Get unique set of APIs from providers
apis = list(sorted(providers.keys()))
return StackRunConfig(
image_name=name,
docker_image=docker_image,
apis=apis,
providers=provider_configs,
metadata_store=SqliteKVStoreConfig.sample_run_config(
__distro_dir__=f"distributions/{name}",
db_name="registry.db",
),
models=self.default_models,
shields=self.default_shields or [],
)
class DistributionTemplate(BaseModel):
"""
Represents a Llama Stack distribution instance that can generate configuration
and documentation files.
"""
name: str
description: str
distro_type: Literal["self_hosted", "remote_hosted", "ondevice"]
providers: Dict[str, List[str]]
run_configs: Dict[str, RunConfigSettings]
template_path: Path
# Optional configuration
run_config_env_vars: Optional[Dict[str, Tuple[str, str]]] = None
docker_image: Optional[str] = None
default_models: Optional[List[ModelInput]] = None
def build_config(self) -> BuildConfig:
return BuildConfig(
name=self.name,
distribution_spec=DistributionSpec(
description=self.description,
docker_image=self.docker_image,
providers=self.providers,
),
image_type="conda", # default to conda, can be overridden
)
def generate_markdown_docs(self) -> str:
providers_table = "| API | Provider(s) |\n"
providers_table += "|-----|-------------|\n"
for api, providers in sorted(self.providers.items()):
providers_str = ", ".join(f"`{p}`" for p in providers)
providers_table += f"| {api} | {providers_str} |\n"
template = self.template_path.read_text()
# Render template with rich-generated table
env = jinja2.Environment(trim_blocks=True, lstrip_blocks=True)
template = env.from_string(template)
return template.render(
name=self.name,
description=self.description,
providers=self.providers,
providers_table=providers_table,
run_config_env_vars=self.run_config_env_vars,
default_models=self.default_models,
)
def save_distribution(self, yaml_output_dir: Path, doc_output_dir: Path) -> None:
for output_dir in [yaml_output_dir, doc_output_dir]:
output_dir.mkdir(parents=True, exist_ok=True)
build_config = self.build_config()
with open(yaml_output_dir / "build.yaml", "w") as f:
yaml.safe_dump(build_config.model_dump(), f, sort_keys=False)
for yaml_pth, settings in self.run_configs.items():
run_config = settings.run_config(
self.name, self.providers, self.docker_image
)
with open(yaml_output_dir / yaml_pth, "w") as f:
yaml.safe_dump(run_config.model_dump(), f, sort_keys=False)
docs = self.generate_markdown_docs()
with open(doc_output_dir / f"{self.name}.md", "w") as f:
f.write(docs)

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .tgi import get_distribution_template # noqa: F401

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version: '2'
name: tgi
distribution_spec:
description: Use TGI for running LLM inference
description: Use (an external) TGI server for running LLM inference
docker_image: llamastack/distribution-tgi:test-0.0.52rc3
providers:
inference: remote::tgi
inference:
- remote::tgi
memory:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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# TGI Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Setting up TGI server
Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
```bash
export INFERENCE_PORT=8080
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export CUDA_VISIBLE_DEVICES=0
docker run --rm -it \
-v $HOME/.cache/huggingface:/data \
-p $INFERENCE_PORT:$INFERENCE_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--cuda-memory-fraction 0.7 \
--model-id $INFERENCE_MODEL \
--port $INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
```bash
export SAFETY_PORT=8081
export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
export CUDA_VISIBLE_DEVICES=1
docker run --rm -it \
-v $HOME/.cache/huggingface:/data \
-p $SAFETY_PORT:$SAFETY_PORT \
--gpus $CUDA_VISIBLE_DEVICES \
ghcr.io/huggingface/text-generation-inference:2.3.1 \
--dtype bfloat16 \
--usage-stats off \
--sharded false \
--model-id $SAFETY_MODEL \
--port $SAFETY_PORT
```
## Running Llama Stack
Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run-with-safety.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env TGI_SAFETY_URL=http://host.docker.internal:$SAFETY_PORT
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template {{ name }} --image-type conda
llama stack run ./run.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml
--port 5001
--env INFERENCE_MODEL=$INFERENCE_MODEL
--env TGI_URL=http://127.0.0.1:$INFERENCE_PORT
--env SAFETY_MODEL=$SAFETY_MODEL
--env TGI_SAFETY_URL=http://127.0.0.1:$SAFETY_PORT
```

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version: '2'
image_name: tgi
docker_image: llamastack/distribution-tgi:test-0.0.52rc3
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: tgi-inference
provider_type: remote::tgi
config:
url: ${env.TGI_URL}
- provider_id: tgi-safety
provider_type: remote::tgi
config:
url: ${env.TGI_SAFETY_URL}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: tgi-inference
provider_model_id: null
- metadata: {}
model_id: ${env.SAFETY_MODEL}
provider_id: tgi-safety
provider_model_id: null
shields:
- params: null
shield_id: ${env.SAFETY_MODEL}
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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version: '2'
image_name: tgi
docker_image: llamastack/distribution-tgi:test-0.0.52rc3
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: tgi-inference
provider_type: remote::tgi
config:
url: ${env.TGI_URL}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: tgi-inference
provider_model_id: null
shields: []
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.remote.inference.tgi import TGIImplConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::tgi"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="tgi-inference",
provider_type="remote::tgi",
config=TGIImplConfig.sample_run_config(
url="${env.TGI_URL}",
),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="tgi-inference",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="tgi-safety",
)
return DistributionTemplate(
name="tgi",
distro_type="self_hosted",
description="Use (an external) TGI server for running LLM inference",
docker_image="llamastack/distribution-tgi:test-0.0.52rc3",
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=[inference_model, safety_model],
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=[inference_model],
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
Provider(
provider_id="tgi-safety",
provider_type="remote::tgi",
config=TGIImplConfig.sample_run_config(
url="${env.TGI_SAFETY_URL}",
),
),
],
},
default_models=[
inference_model,
safety_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the TGI server",
),
"TGI_URL": (
"http://127.0.0.1:8080}/v1",
"URL of the TGI server with the main inference model",
),
"TGI_SAFETY_URL": (
"http://127.0.0.1:8081/v1",
"URL of the TGI server with the safety model",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Name of the safety (Llama-Guard) model to use",
),
},
)

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .together import get_distribution_template # noqa: F401

20
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@ -1,11 +1,19 @@
version: '2'
name: together
distribution_spec:
description: Use Together.ai for running LLM inference
description: Use Together.AI for running LLM inference
docker_image: null
providers:
inference: remote::together
inference:
- remote::together
memory:
- inline::faiss
- remote::weaviate
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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# Fireworks Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
{{ providers_table }}
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
{% if default_models %}
### Models
The following models are available by default:
{% for model in default_models %}
- `{{ model.model_id }}`
{% endfor %}
{% endif %}
### Prerequisite: API Keys
Make sure you have access to a Together API Key. You can get one by visiting [together.xyz](https://together.xyz/).
## Running Llama Stack with Together
You can do this via Conda (build code) or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ./run.yaml:/root/my-run.yaml \
llamastack/distribution-{{ name }} \
/root/my-run.yaml \
--port $LLAMA_STACK_PORT \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```
### Via Conda
```bash
llama stack build --template together --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env TOGETHER_API_KEY=$TOGETHER_API_KEY
```

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version: '2'
image_name: together
docker_image: null
conda_env: null
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: together
provider_type: remote::together
config:
url: https://api.together.xyz/v1
api_key: ${env.TOGETHER_API_KEY}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/registry.db
models:
- metadata: {}
model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Meta-Llama-Guard-3-8B
provider_id: null
provider_model_id: null
- metadata: {}
model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo
provider_id: null
provider_model_id: null
shields:
- params: null
shield_id: meta-llama/Llama-Guard-3-1B
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.remote.inference.together import TogetherImplConfig
from llama_stack.providers.remote.inference.together.together import MODEL_ALIASES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::together"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="together",
provider_type="remote::together",
config=TogetherImplConfig.sample_run_config(),
)
default_models = [ModelInput(model_id=m.provider_model_id) for m in MODEL_ALIASES]
return DistributionTemplate(
name="together",
distro_type="self_hosted",
description="Use Together.AI for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=default_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=default_models,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-1B")],
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"TOGETHER_API_KEY": (
"",
"Together.AI API Key",
),
},
)